# core/training/iterative_trainer.py
class IterativeTrainer:
    def __init__(self, model, deepseek_adapter):
        self.model = model
        self.deepseek_adapter = deepseek_adapter
        self.optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5)

    def train_step(self, batch):
        # 初始预测
        predictions = self.model(batch['inputs'])

        # DeepSeek验证
        with torch.no_grad():
            feedback = self.deepseek_adapter.get_feedback(
                predictions,
                batch['requirements']
            )

        # 生成修正目标
        corrected_targets = self._apply_feedback(batch['targets'], feedback)

        # 计算修正损失
        loss = self.model.compute_loss(predictions, corrected_targets)

        # 反向传播
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

        return loss.item()

    def _apply_feedback(self, targets, feedback):
        """将DeepSeek反馈融入训练目标"""
        # 实现反馈融合逻辑
        return adjusted_targets